Strategic Insights into Private Group Tour Satisfaction: Evidence from Online Reviews and AI-Driven Modeling

With the rapid adoption of “Internet Plus” technologies in the tourism industry, online purchasing of travel products has become increasingly common, and user-generated reviews now play a vital role in consumer decision-making. This study develops a customer satisfaction evaluation model for customized online tour packages by combining text mining, Latent Dirichlet Allocation (LDA), and focus group interviews. The DEMATEL and DANP methods are used to examine the relationships and relative importance among key factors. Results show that product experience is the most influential factor affecting customer satisfaction, with product attributes and service quality having a significant impact on the overall experience. Based on these findings, the study provides targeted recommendations for product improvement. It suggests future research directions, including the integration of data from multiple platforms and the conduct of behavioral validation to enhance the model’s usefulness.